Common Use Cases

The future of data analysis and machine-learning in automotive applications offers many possibilities and challenges. Our solutions focus on promoting process automation and predictive metrics by using open data methods to create data tools and capabilities, setting up strategies to gather and comprehend the information gathered in order to enhance decisions and learn from it.

We apply predictive analysis to answer your business challenges and achieve measurable operational efficiencies by examining a multitude of internal data sources (registrations, orders, smartphone usage, customer profiles, itineraries, complaints, sensor data) and unlocking the potential benefits of combining with external data sets (research, social media, magazines, weather, traffic, news events). In addition, our logistics analysis can be applied to anticipate the behavior of both machines and people, and take into account these external variables. This process optimization reduces mechanical downtime, enhances the efficiency of freight routes and increases client satisfaction.

Predictive Analysis

Move beyond the conventional business intelligence and reporting steps with greater investment in data and analysis. Use analysis to solve customer-centric business problems by using data science to understand and quantify behaviors, likes, dislikes, patterns, and preferences, and predict their value. 

We build dedicated models and tools to aggregate, manage, cleanse and predict core business and revenue outcomes. We use operational data sources as well as non-transportation related sources such as traffic data, passenger sentiment, customer data, geo-spatial data, weather data, and retailer information to improve operational management and transportation planning activities.

Efficiency: Time series models and classification models can be deployed to predict the risk of resource and service imbalances. Detailed daily forecasts can be generated at each stage to inform decision-makers and operations planning.

Resources and Inventory: Real-time distribution, resources, and supply patterns can be modeled to monitor the entire operational and distribution network, enhancing productivity and enabling faster and more efficient decision making.

Route Modeling: Identify daily events, such as transportation delays, failures, or system interruptions, that have the greatest economic impact or cost to your business.

Consumer insight and analysis

Apply analysis to solve customer-centric business challenges by using data science to understand and quantify behaviors, preferences, patterns, interests, and predict their value. Understand purchasing and demand patterns to better manage logistics and resources. 

Common use cases involve: Reducing customer churn Increasing cross-selling and aggregation of services Increasing customer satisfaction Improving customer acquisition Preventing customer loss and targeted reconquest Predicting resources and inventories

Sensor automation and data

Maintenance and safety

Real-time analysis of a continuous stream of data on every aspect of the trip (e.g. passenger behavior, environment, engineering performance, etc.) allows our dedicated transportation data experts to create complex algorithms to predict problems and, better yet, prevent them.


Automation of routine tasks, sequence of events, and triggers, eliminating the need for manual data input, transcripts, sentiment surveys, and similar costly and error-prone processes. Create customized solutions that allow your staff to focus on creative tasks, strategy, and key business challenges. 

Intelligent Sensors

Implement IoT framework and predictive algorithms to track key events and information. 

get started?

Our experts and software engineers have worked with industry players, collecting and analyze information to transform business practices. We’d like to help you create your next business solution, whether it’s migrating existing platforms or developing new systems. Technology is changing rapidly, let’s build robust business solutions.

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